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Core Neural Networks Framework
/*
* Copyright (c) 2018 by Andrew Charneski.
*
* The author licenses this file to you under the
* Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance
* with the License. You may obtain a copy
* of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package com.simiacryptus.mindseye.opt;
import com.simiacryptus.lang.TimedResult;
import com.simiacryptus.mindseye.eval.*;
import com.simiacryptus.mindseye.lang.*;
import com.simiacryptus.mindseye.layers.StochasticComponent;
import com.simiacryptus.mindseye.network.DAGNetwork;
import com.simiacryptus.mindseye.opt.line.ArmijoWolfeSearch;
import com.simiacryptus.mindseye.opt.line.FailsafeLineSearchCursor;
import com.simiacryptus.mindseye.opt.line.LineSearchCursor;
import com.simiacryptus.mindseye.opt.line.LineSearchStrategy;
import com.simiacryptus.mindseye.opt.orient.LBFGS;
import com.simiacryptus.mindseye.opt.orient.OrientationStrategy;
import com.simiacryptus.util.FastRandom;
import com.simiacryptus.util.Util;
import com.simiacryptus.util.data.DoubleStatistics;
import javax.annotation.Nonnull;
import java.lang.management.ManagementFactory;
import java.time.Duration;
import java.time.temporal.ChronoUnit;
import java.time.temporal.TemporalUnit;
import java.util.*;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
import java.util.function.Function;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
/**
* This training class attempts to manage the sample size hyperparameter and convergence criteria via the periodic
* evaluation of a validation function. It uses an itermediate "epoch" loop where a target learning improvement and
* overtraining ratio are sought. It also supports multiple stages to each epoch, allowing use cases such as GAN and
* layerwise training.
*/
public class ValidatingTrainer {
private final AtomicInteger disappointments = new AtomicInteger(0);
@Nonnull
private final List regimen;
private final AtomicLong trainingMeasurementTime = new AtomicLong(0);
private final AtomicLong validatingMeasurementTime = new AtomicLong(0);
@Nonnull
private final Trainable validationSubject;
private double adjustmentFactor = 0.5;
private double adjustmentTolerance = 0.1;
private AtomicInteger currentIteration = new AtomicInteger(0);
private int disappointmentThreshold = 0;
private int epochIterations = 1;
private int improvmentStaleThreshold = 3;
private int maxEpochIterations = 20;
private int maxIterations = Integer.MAX_VALUE;
private int maxTrainingSize = Integer.MAX_VALUE;
private int minEpochIterations = 1;
private int minTrainingSize = 100;
private TrainingMonitor monitor = new TrainingMonitor();
private double overtrainingTarget = 2;
private double pessimism = 10;
private double terminateThreshold;
private Duration timeout;
private int trainingSize = 10000;
private double trainingTarget = 0.7;
/**
* Instantiates a new Validating trainer.
*
* @param trainingSubject the training subject
* @param validationSubject the validation subject
*/
public ValidatingTrainer(@Nonnull final SampledTrainable trainingSubject, @Nonnull final Trainable validationSubject) {
regimen = new ArrayList(Arrays.asList(new TrainingPhase(new PerformanceWrapper(trainingSubject))));
validationSubject.addRef();
this.validationSubject = new TrainableBase() {
@Override
protected void _free() {
validationSubject.freeRef();
}
@Override
public PointSample measure(final TrainingMonitor monitor) {
@Nonnull final TimedResult time = TimedResult.time(() ->
validationSubject.measure(monitor)
);
validatingMeasurementTime.addAndGet(time.timeNanos);
return time.result;
}
@Override
public boolean reseed(final long seed) {
return validationSubject.reseed(seed);
}
@Override
public Layer getLayer() {
return validationSubject.getLayer();
}
};
trainingSize = trainingSubject.getTrainingSize();
timeout = Duration.of(5, ChronoUnit.MINUTES);
terminateThreshold = Double.NEGATIVE_INFINITY;
}
@Nonnull
private static CharSequence getId(@Nonnull final DoubleBuffer x) {
return x.key.toString();
}
private String compare(@Nonnull final PointSample previousPoint, @Nonnull final PointSample nextPoint) {
@Nonnull final StateSet nextWeights = nextPoint.weights;
@Nonnull final StateSet prevWeights = previousPoint.weights;
return String.format("Overall network state change: %s", prevWeights.stream()
.collect(Collectors.groupingBy(x -> x, Collectors.toList())).entrySet().stream()
.collect(Collectors.toMap(x -> x.getKey(), list -> {
final List doubleList = list.getValue().stream().map(prevWeight -> {
final DoubleBuffer dirDelta = nextWeights.getMap().get(prevWeight.key);
final double numerator = prevWeight.deltaStatistics().rms();
final double denominator = null == dirDelta ? 0 : dirDelta.deltaStatistics().rms();
return numerator / (0 == denominator ? 1 : denominator);
}).collect(Collectors.toList());
if (1 == doubleList.size())
return Double.toString(doubleList.get(0));
return new DoubleStatistics().accept(doubleList.stream().mapToDouble(x -> x).toArray()).toString();
})));
}
/**
* Gets adjustment factor.
*
* @return the adjustment factor
*/
public double getAdjustmentFactor() {
return adjustmentFactor;
}
/**
* Sets adjustment factor.
*
* @param adjustmentFactor the adjustment factor
* @return the adjustment factor
*/
@Nonnull
public ValidatingTrainer setAdjustmentFactor(final double adjustmentFactor) {
this.adjustmentFactor = adjustmentFactor;
return this;
}
/**
* Gets adjustment tolerance.
*
* @return the adjustment tolerance
*/
public double getAdjustmentTolerance() {
return adjustmentTolerance;
}
/**
* Sets adjustment tolerance.
*
* @param adjustmentTolerance the adjustment tolerance
* @return the adjustment tolerance
*/
@Nonnull
public ValidatingTrainer setAdjustmentTolerance(final double adjustmentTolerance) {
this.adjustmentTolerance = adjustmentTolerance;
return this;
}
/**
* Gets current iteration.
*
* @return the current iteration
*/
public AtomicInteger getCurrentIteration() {
return currentIteration;
}
/**
* Sets current iteration.
*
* @param currentIteration the current iteration
* @return the current iteration
*/
@Nonnull
public ValidatingTrainer setCurrentIteration(final AtomicInteger currentIteration) {
this.currentIteration = currentIteration;
return this;
}
/**
* Gets disappointment threshold.
*
* @return the disappointment threshold
*/
public int getDisappointmentThreshold() {
return disappointmentThreshold;
}
/**
* Sets disappointment threshold.
*
* @param disappointmentThreshold the disappointment threshold
*/
public void setDisappointmentThreshold(final int disappointmentThreshold) {
this.disappointmentThreshold = disappointmentThreshold;
}
/**
* Gets runPhase iterations.
*
* @return the runPhase iterations
*/
public int getEpochIterations() {
return epochIterations;
}
/**
* Sets runPhase iterations.
*
* @param epochIterations the runPhase iterations
* @return the runPhase iterations
*/
@Nonnull
public ValidatingTrainer setEpochIterations(final int epochIterations) {
this.epochIterations = epochIterations;
return this;
}
/**
* Gets improvment stale threshold.
*
* @return the improvment stale threshold
*/
public int getImprovmentStaleThreshold() {
return improvmentStaleThreshold;
}
/**
* Sets improvment stale threshold.
*
* @param improvmentStaleThreshold the improvment stale threshold
*/
public void setImprovmentStaleThreshold(final int improvmentStaleThreshold) {
this.improvmentStaleThreshold = improvmentStaleThreshold;
}
/**
* Gets max runPhase iterations.
*
* @return the max runPhase iterations
*/
public int getMaxEpochIterations() {
return maxEpochIterations;
}
/**
* Sets max runPhase iterations.
*
* @param maxEpochIterations the max runPhase iterations
* @return the max runPhase iterations
*/
@Nonnull
public ValidatingTrainer setMaxEpochIterations(final int maxEpochIterations) {
this.maxEpochIterations = maxEpochIterations;
return this;
}
/**
* Gets max iterations.
*
* @return the max iterations
*/
public int getMaxIterations() {
return maxIterations;
}
/**
* Sets max iterations.
*
* @param maxIterations the max iterations
* @return the max iterations
*/
@Nonnull
public ValidatingTrainer setMaxIterations(final int maxIterations) {
this.maxIterations = maxIterations;
return this;
}
/**
* Gets max training size.
*
* @return the max training size
*/
public int getMaxTrainingSize() {
return maxTrainingSize;
}
/**
* Sets max training size.
*
* @param maxTrainingSize the max training size
* @return the max training size
*/
@Nonnull
public ValidatingTrainer setMaxTrainingSize(final int maxTrainingSize) {
this.maxTrainingSize = maxTrainingSize;
return this;
}
/**
* Gets min runPhase iterations.
*
* @return the min runPhase iterations
*/
public int getMinEpochIterations() {
return minEpochIterations;
}
/**
* Sets min runPhase iterations.
*
* @param minEpochIterations the min runPhase iterations
* @return the min runPhase iterations
*/
@Nonnull
public ValidatingTrainer setMinEpochIterations(final int minEpochIterations) {
this.minEpochIterations = minEpochIterations;
return this;
}
/**
* Gets min training size.
*
* @return the min training size
*/
public int getMinTrainingSize() {
return minTrainingSize;
}
/**
* Sets min training size.
*
* @param minTrainingSize the min training size
* @return the min training size
*/
@Nonnull
public ValidatingTrainer setMinTrainingSize(final int minTrainingSize) {
this.minTrainingSize = minTrainingSize;
return this;
}
/**
* Gets monitor.
*
* @return the monitor
*/
public TrainingMonitor getMonitor() {
return monitor;
}
/**
* Sets monitor.
*
* @param monitor the monitor
* @return the monitor
*/
@Nonnull
public ValidatingTrainer setMonitor(final TrainingMonitor monitor) {
this.monitor = monitor;
return this;
}
/**
* Gets overtraining target.
*
* @return the overtraining target
*/
public double getOvertrainingTarget() {
return overtrainingTarget;
}
/**
* Sets overtraining target.
*
* @param overtrainingTarget the overtraining target
* @return the overtraining target
*/
@Nonnull
public ValidatingTrainer setOvertrainingTarget(final double overtrainingTarget) {
this.overtrainingTarget = overtrainingTarget;
return this;
}
/**
* Gets pessimism.
*
* @return the pessimism
*/
public double getPessimism() {
return pessimism;
}
/**
* Sets pessimism.
*
* @param pessimism the pessimism
* @return the pessimism
*/
@Nonnull
public ValidatingTrainer setPessimism(final double pessimism) {
this.pessimism = pessimism;
return this;
}
/**
* Gets regimen.
*
* @return the regimen
*/
@Nonnull
public List getRegimen() {
return regimen;
}
/**
* Gets terminate threshold.
*
* @return the terminate threshold
*/
public double getTerminateThreshold() {
return terminateThreshold;
}
/**
* Sets terminate threshold.
*
* @param terminateThreshold the terminate threshold
* @return the terminate threshold
*/
@Nonnull
public ValidatingTrainer setTerminateThreshold(final double terminateThreshold) {
this.terminateThreshold = terminateThreshold;
return this;
}
/**
* Gets timeout.
*
* @return the timeout
*/
public Duration getTimeout() {
return timeout;
}
/**
* Sets timeout.
*
* @param timeout the timeout
* @return the timeout
*/
@Nonnull
public ValidatingTrainer setTimeout(final Duration timeout) {
this.timeout = timeout;
return this;
}
/**
* Gets training size.
*
* @return the training size
*/
public int getTrainingSize() {
return trainingSize;
}
/**
* Sets training size.
*
* @param trainingSize the training size
* @return the training size
*/
@Nonnull
public ValidatingTrainer setTrainingSize(final int trainingSize) {
this.trainingSize = trainingSize;
return this;
}
/**
* Gets training target.
*
* @return the training target
*/
public double getTrainingTarget() {
return trainingTarget;
}
/**
* Sets training target.
*
* @param trainingTarget the training target
* @return the training target
*/
@Nonnull
public ValidatingTrainer setTrainingTarget(final double trainingTarget) {
this.trainingTarget = trainingTarget;
return this;
}
/**
* Gets validation subject.
*
* @return the validation subject
*/
@Nonnull
public Trainable getValidationSubject() {
return validationSubject;
}
private PointSample measure(@Nonnull final TrainingPhase phase) {
int retries = 0;
do {
if (10 < retries++) throw new IterativeStopException();
final PointSample currentPoint = phase.trainingSubject.measure(monitor);
if (Double.isFinite(currentPoint.getMean())) return currentPoint;
phase.orientation.reset();
} while (true);
}
@Nonnull
private ValidatingTrainer reset(@Nonnull final TrainingPhase phase, final long seed) {
if (!phase.trainingSubject.reseed(seed)) throw new IterativeStopException();
phase.orientation.reset();
phase.trainingSubject.reseed(seed);
if (phase.trainingSubject.getLayer() instanceof DAGNetwork) {
((DAGNetwork) phase.trainingSubject.getLayer()).visitLayers(layer -> {
if (layer instanceof StochasticComponent)
((StochasticComponent) layer).shuffle(StochasticComponent.random.get().nextLong());
});
}
return this;
}
/**
* Run double.
*
* @return the double
*/
public double run() {
try {
final long timeoutAt = System.currentTimeMillis() + timeout.toMillis();
if (validationSubject.getLayer() instanceof DAGNetwork) {
((DAGNetwork) validationSubject.getLayer()).visitLayers(layer -> {
if (layer instanceof StochasticComponent) ((StochasticComponent) layer).clearNoise();
});
}
@Nonnull final EpochParams epochParams = new EpochParams(timeoutAt, epochIterations, getTrainingSize(), validationSubject.measure(monitor));
int epochNumber = 0;
int iterationNumber = 0;
int lastImprovement = 0;
double lowestValidation = Double.POSITIVE_INFINITY;
while (true) {
if (shouldHalt(monitor, timeoutAt)) {
monitor.log("Training halted");
break;
}
monitor.log(String.format("Epoch parameters: %s, %s", epochParams.trainingSize, epochParams.iterations));
@Nonnull final List regimen = getRegimen();
final long seed = System.nanoTime();
final List epochResults = IntStream.range(0, regimen.size()).mapToObj(i -> {
final TrainingPhase phase = getRegimen().get(i);
return runPhase(epochParams, phase, i, seed);
}).collect(Collectors.toList());
final EpochResult primaryPhase = epochResults.get(0);
iterationNumber += primaryPhase.iterations;
final double trainingDelta = primaryPhase.currentPoint.getMean() / primaryPhase.priorMean;
if (validationSubject.getLayer() instanceof DAGNetwork) {
((DAGNetwork) validationSubject.getLayer()).visitLayers(layer -> {
if (layer instanceof StochasticComponent) ((StochasticComponent) layer).clearNoise();
});
}
final PointSample currentValidation = validationSubject.measure(monitor);
final double overtraining = Math.log(trainingDelta) / Math.log(currentValidation.getMean() / epochParams.validation.getMean());
final double validationDelta = currentValidation.getMean() / epochParams.validation.getMean();
final double adj1 = Math.pow(Math.log(getTrainingTarget()) / Math.log(validationDelta), adjustmentFactor);
final double adj2 = Math.pow(overtraining / getOvertrainingTarget(), adjustmentFactor);
final double validationMean = currentValidation.getMean();
if (validationMean < lowestValidation) {
lowestValidation = validationMean;
lastImprovement = iterationNumber;
}
monitor.log(String.format("Epoch %d result apply %s iterations, %s/%s samples: {validation *= 2^%.5f; training *= 2^%.3f; Overtraining = %.2f}, {itr*=%.2f, len*=%.2f} %s since improvement; %.4f validation time",
++epochNumber, primaryPhase.iterations, epochParams.trainingSize, getMaxTrainingSize(),
Math.log(validationDelta) / Math.log(2), Math.log(trainingDelta) / Math.log(2),
overtraining, adj1, adj2, iterationNumber - lastImprovement,
validatingMeasurementTime.getAndSet(0) / 1e9));
if (!primaryPhase.continueTraining) {
monitor.log(String.format("Training %d runPhase halted", epochNumber));
break;
}
if (epochParams.trainingSize >= getMaxTrainingSize()) {
final double roll = FastRandom.INSTANCE.random();
if (roll > Math.pow(2 - validationDelta, pessimism)) {
monitor.log(String.format("Training randomly converged: %3f", roll));
break;
} else {
if (iterationNumber - lastImprovement > improvmentStaleThreshold) {
if (disappointments.incrementAndGet() > getDisappointmentThreshold()) {
monitor.log(String.format("Training converged after %s iterations", iterationNumber - lastImprovement));
break;
} else {
monitor.log(String.format("Training failed to converged on %s attempt after %s iterations", disappointments.get(), iterationNumber - lastImprovement));
}
} else {
disappointments.set(0);
}
}
}
if (validationDelta < 1.0 && trainingDelta < 1.0) {
if (adj1 < 1 - adjustmentTolerance || adj1 > 1 + adjustmentTolerance) {
epochParams.iterations = Math.max(getMinEpochIterations(), Math.min(getMaxEpochIterations(), (int) (primaryPhase.iterations * adj1)));
}
if (adj2 < 1 + adjustmentTolerance || adj2 > 1 - adjustmentTolerance) {
epochParams.trainingSize = Math.max(0, Math.min(Math.max(getMinTrainingSize(), Math.min(getMaxTrainingSize(), (int) (epochParams.trainingSize * adj2))), epochParams.trainingSize));
}
} else {
epochParams.trainingSize = Math.max(0, Math.min(Math.max(getMinTrainingSize(), Math.min(getMaxTrainingSize(), epochParams.trainingSize * 5)), epochParams.trainingSize));
epochParams.iterations = 1;
}
epochParams.validation = currentValidation;
}
if (validationSubject.getLayer() instanceof DAGNetwork) {
((DAGNetwork) validationSubject.getLayer()).visitLayers(layer -> {
if (layer instanceof StochasticComponent) ((StochasticComponent) layer).clearNoise();
});
}
return epochParams.validation.getMean();
} catch (@Nonnull final Throwable e) {
throw new RuntimeException(e);
}
}
/**
* Epoch runPhase result.
*
* @param epochParams the runPhase params
* @param phase the phase
* @param i the
* @param seed the seed
* @return the runPhase result
*/
@Nonnull
protected EpochResult runPhase(@Nonnull final EpochParams epochParams, @Nonnull final TrainingPhase phase, final int i, final long seed) {
monitor.log(String.format("Phase %d: %s", i, phase));
phase.trainingSubject.setTrainingSize(epochParams.trainingSize);
monitor.log(String.format("resetAndMeasure; trainingSize=%s", epochParams.trainingSize));
PointSample currentPoint = reset(phase, seed).measure(phase);
final double pointMean = currentPoint.getMean();
assert 0 < currentPoint.delta.getMap().size() : "Nothing to optimize";
int step = 1;
for (; step <= epochParams.iterations || epochParams.iterations <= 0; step++) {
if (shouldHalt(monitor, epochParams.timeoutMs)) {
return new EpochResult(false, pointMean, currentPoint, step);
}
final long startTime = System.nanoTime();
final long prevGcTime = ManagementFactory.getGarbageCollectorMXBeans().stream().mapToLong(x -> x.getCollectionTime()).sum();
@Nonnull final StepResult epoch = runStep(currentPoint, phase);
final long newGcTime = ManagementFactory.getGarbageCollectorMXBeans().stream().mapToLong(x -> x.getCollectionTime()).sum();
final long endTime = System.nanoTime();
final CharSequence performance = String.format("%s in %.3f seconds; %.3f in orientation, %.3f in gc, %.3f in line search; %.3f trainAll time",
epochParams.trainingSize, (endTime - startTime) / 1e9,
epoch.performance[0],
(newGcTime - prevGcTime) / 1e3,
epoch.performance[1],
trainingMeasurementTime.getAndSet(0) / 1e9
);
currentPoint = epoch.currentPoint.setRate(0.0);
if (epoch.previous.getMean() <= epoch.currentPoint.getMean()) {
monitor.log(String.format("Iteration %s failed, aborting. Error: %s (%s)",
currentIteration.get(), epoch.currentPoint.getMean(), performance));
return new EpochResult(false, pointMean, currentPoint, step);
} else {
monitor.log(String.format("Iteration %s complete. Error: %s (%s)", currentIteration.get(), epoch.currentPoint.getMean(), performance));
}
monitor.onStepComplete(new Step(currentPoint, currentIteration.get()));
}
return new EpochResult(true, pointMean, currentPoint, step);
}
/**
* Step runStep result.
*
* @param previousPoint the previous point
* @param phase the phase
* @return the runStep result
*/
@Nonnull
protected StepResult runStep(@Nonnull final PointSample previousPoint, @Nonnull final TrainingPhase phase) {
currentIteration.incrementAndGet();
@Nonnull final TimedResult timedOrientation = TimedResult.time(() -> phase.orientation.orient(phase.trainingSubject, previousPoint, monitor));
final LineSearchCursor direction = timedOrientation.result;
final CharSequence directionType = direction.getDirectionType();
LineSearchStrategy lineSearchStrategy;
if (phase.lineSearchStrategyMap.containsKey(directionType)) {
lineSearchStrategy = phase.lineSearchStrategyMap.get(directionType);
} else {
monitor.log(String.format("Constructing line search parameters: %s", directionType));
lineSearchStrategy = phase.lineSearchFactory.apply(direction.getDirectionType());
phase.lineSearchStrategyMap.put(directionType, lineSearchStrategy);
}
@Nonnull final TimedResult timedLineSearch = TimedResult.time(() -> {
@Nonnull final FailsafeLineSearchCursor cursor = new FailsafeLineSearchCursor(direction, previousPoint, monitor);
lineSearchStrategy.step(cursor, monitor);
@Nonnull final PointSample restore = cursor.getBest(monitor).restore();
//cursor.step(restore.rate, monitor);
return restore;
});
final PointSample bestPoint = timedLineSearch.result;
if (bestPoint.getMean() > previousPoint.getMean()) {
throw new IllegalStateException(bestPoint.getMean() + " > " + previousPoint.getMean());
}
monitor.log(compare(previousPoint, bestPoint));
return new StepResult(previousPoint, bestPoint, new double[]{timedOrientation.timeNanos / 1e9, timedLineSearch.timeNanos / 1e9});
}
/**
* Sets line search factory.
*
* @param lineSearchFactory the line search factory
* @return the line search factory
*/
@Nonnull
@Deprecated
public ValidatingTrainer setLineSearchFactory(final Function lineSearchFactory) {
getRegimen().get(0).setLineSearchFactory(lineSearchFactory);
return this;
}
/**
* Sets orientation.
*
* @param orientation the orientation
* @return the orientation
*/
@Nonnull
@Deprecated
public ValidatingTrainer setOrientation(final OrientationStrategy orientation) {
getRegimen().get(0).setOrientation(orientation);
return this;
}
/**
* Sets timeout.
*
* @param number the number
* @param units the units
* @return the timeout
*/
@Nonnull
public ValidatingTrainer setTimeout(final int number, @Nonnull final TemporalUnit units) {
timeout = Duration.of(number, units);
return this;
}
/**
* Sets timeout.
*
* @param number the number
* @param units the units
* @return the timeout
*/
@Nonnull
public ValidatingTrainer setTimeout(final int number, @Nonnull final TimeUnit units) {
return setTimeout(number, Util.cvt(units));
}
/**
* Should halt boolean.
*
* @param monitor the monitor
* @param timeoutMs the timeout ms
* @return the boolean
*/
protected boolean shouldHalt(@Nonnull final TrainingMonitor monitor, final long timeoutMs) {
System.currentTimeMillis();
if (timeoutMs < System.currentTimeMillis()) {
monitor.log("Training timeout");
return true;
} else if (currentIteration.get() > maxIterations) {
monitor.log("Training iteration overflow");
return true;
} else {
return false;
}
}
private static class EpochParams {
/**
* The Iterations.
*/
int iterations;
/**
* The Timeout ms.
*/
long timeoutMs;
/**
* The Training size.
*/
int trainingSize;
/**
* The Validation.
*/
PointSample validation;
private EpochParams(final long timeoutMs, final int iterations, final int trainingSize, final PointSample validation) {
this.timeoutMs = timeoutMs;
this.iterations = iterations;
this.trainingSize = trainingSize;
this.validation = validation;
}
}
private static class EpochResult {
/**
* The Continue training.
*/
boolean continueTraining;
/**
* The Current point.
*/
PointSample currentPoint;
/**
* The Iterations.
*/
int iterations;
/**
* The Prior point.
*/
double priorMean;
/**
* Instantiates a new Epoch result.
*
* @param continueTraining the continue training
* @param priorMean the prior point
* @param currentPoint the current point
* @param iterations the iterations
*/
public EpochResult(final boolean continueTraining, final double priorMean, final PointSample currentPoint, final int iterations) {
this.priorMean = priorMean;
this.currentPoint = currentPoint;
this.continueTraining = continueTraining;
this.iterations = iterations;
}
}
/**
* The type Training phase.
*/
public static class TrainingPhase {
private Function lineSearchFactory = (s) -> new ArmijoWolfeSearch();
private Map lineSearchStrategyMap = new HashMap<>();
private OrientationStrategy orientation = new LBFGS();
private SampledTrainable trainingSubject;
/**
* Instantiates a new Training phase.
*
* @param trainingSubject the training subject
*/
public TrainingPhase(final SampledTrainable trainingSubject) {
setTrainingSubject(trainingSubject);
}
/**
* Gets line search factory.
*
* @return the line search factory
*/
public Function getLineSearchFactory() {
return lineSearchFactory;
}
/**
* Sets line search factory.
*
* @param lineSearchFactory the line search factory
* @return the line search factory
*/
@Nonnull
public TrainingPhase setLineSearchFactory(final Function lineSearchFactory) {
this.lineSearchFactory = lineSearchFactory;
return this;
}
/**
* Gets line search strategy buildMap.
*
* @return the line search strategy buildMap
*/
public Map getLineSearchStrategyMap() {
return lineSearchStrategyMap;
}
/**
* Sets line search strategy buildMap.
*
* @param lineSearchStrategyMap the line search strategy buildMap
* @return the line search strategy buildMap
*/
@Nonnull
public TrainingPhase setLineSearchStrategyMap(final Map lineSearchStrategyMap) {
this.lineSearchStrategyMap = lineSearchStrategyMap;
return this;
}
/**
* Gets orientation.
*
* @return the orientation
*/
public OrientationStrategy getOrientation() {
return orientation;
}
/**
* Sets orientation.
*
* @param orientation the orientation
* @return the orientation
*/
@Nonnull
public TrainingPhase setOrientation(final OrientationStrategy orientation) {
this.orientation = orientation;
return this;
}
/**
* Gets training subject.
*
* @return the training subject
*/
public SampledTrainable getTrainingSubject() {
return trainingSubject;
}
/**
* Sets training subject.
*
* @param trainingSubject the training subject
* @return the training subject
*/
@Nonnull
public TrainingPhase setTrainingSubject(final SampledTrainable trainingSubject) {
this.trainingSubject = trainingSubject;
return this;
}
@Nonnull
@Override
public String toString() {
return "TrainingPhase{" +
"trainingSubject=" + trainingSubject +
", orientation=" + orientation +
'}';
}
}
private class PerformanceWrapper extends TrainableWrapper implements SampledTrainable {
/**
* Instantiates a new Performance wrapper.
*
* @param trainingSubject the training subject
*/
public PerformanceWrapper(final SampledTrainable trainingSubject) {
super(trainingSubject);
}
@Nonnull
@Override
public SampledCachedTrainable cached() {
return new SampledCachedTrainable<>(this);
}
@Override
public int getTrainingSize() {
return getInner().getTrainingSize();
}
@Override
public PointSample measure(final TrainingMonitor monitor) {
@Nonnull final TimedResult time = TimedResult.time(() ->
getInner().measure(monitor)
);
trainingMeasurementTime.addAndGet(time.timeNanos);
return time.result;
}
@Nonnull
@Override
public SampledTrainable setTrainingSize(final int trainingSize) {
getInner().setTrainingSize(trainingSize);
return this;
}
}
private class StepResult {
/**
* The Current point.
*/
final PointSample currentPoint;
/**
* The Performance.
*/
final double[] performance;
/**
* The Previous.
*/
final PointSample previous;
/**
* Instantiates a new Step result.
*
* @param previous the previous
* @param currentPoint the current point
* @param performance the performance
*/
public StepResult(final PointSample previous, final PointSample currentPoint, final double[] performance) {
this.currentPoint = currentPoint;
this.previous = previous;
this.performance = performance;
}
}
}
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